Olmo 3.1 32B Instruct vs Claude Mythos 5.1

At a Glance

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Pricing and Limits
Input priceFrom · USD / 1M tokensNot reported$10.00Anthropic · Sep 2, 2026
Output priceFrom · USD / 1M tokensNot reported$50.00Anthropic · Sep 2, 2026
Context windowMaximum documented tokens66K1,000K
Model facts checkedAug 28, 2026View model evidence →Sep 2, 2026View model evidence →

Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →

Available Benchmarks

All benchmark results →
No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.

Side-by-Side Facts

FieldOlmo-3.1-32B-InstructClaude Mythos 5.1
DeveloperAi2Anthropic
FamilyOlmo 3 1 32b InstructClaude 5 1
ModelOlmo-3.1-32B-InstructClaude Mythos 5.1
VersionOlmo-3.1-32B-InstructClaude Mythos 5.1
Lifecycleactiveactive
ReleasedUnknown2026-09-01
Knowledge cutoffUnknown2026-06-01
Input modalitiesTextText, Image
Output modalitiesTextText
Context window66K1,000K
Total parameters32.2BUnknown
Active parametersUnknownUnknown
Licenseapache-2.0Unknown
Open weightsYesNo
API availableUnknownYes
Self-hostableYesNo
Provider accessUnknownAnthropic (Project Glasswing)
Capabilitieschat, generation, toolschat, generation, reasoning, tools

Olmo 3.1 32B Instruct Capabilities

chatgenerationtools
Serving providers0
Canonical IDallenai/Olmo-3.1-32B-Instruct

Claude Mythos 5.1 Capabilities

chatgenerationreasoningtools
Serving providers1
Canonical IDanthropic/claude-mythos-5-1

Primary Evidence

Sources and Freshness

Questions

Olmo 3.1 32B Instruct vs Claude Mythos 5.1 FAQs

Is Olmo 3.1 32B Instruct or Claude Mythos 5.1 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Olmo 3.1 32B Instruct and Claude Mythos 5.1, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Olmo 3.1 32B Instruct or Claude Mythos 5.1?+

Only Claude Mythos 5.1 has a directly sourced input price: $10.00 per million tokens. Only Claude Mythos 5.1 has a directly sourced output price: $50.00 per million tokens.

Which has a larger context window, Olmo 3.1 32B Instruct or Claude Mythos 5.1?+

Claude Mythos 5.1 has the larger sourced context window. Olmo 3.1 32B Instruct supports 66K and Claude Mythos 5.1 supports 1,000K.

Which performs better in benchmarks, Olmo 3.1 32B Instruct or Claude Mythos 5.1?+

There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.

Can Olmo 3.1 32B Instruct or Claude Mythos 5.1 be self-hosted?+

Olmo 3.1 32B Instruct is the only model in this pair currently marked as self-hostable. Olmo 3.1 32B Instruct is open weight; Claude Mythos 5.1 is not marked open weight.

Can Olmo 3.1 32B Instruct and Claude Mythos 5.1 understand images?+

Olmo 3.1 32B Instruct is not documented with image input; Claude Mythos 5.1 is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Olmo 3.1 32B Instruct or Claude Mythos 5.1?+

Claude Mythos 5.1 has the larger sourced maximum output: Olmo 3.1 32B Instruct supports 33K and Claude Mythos 5.1 supports 128K output tokens.

Do Olmo 3.1 32B Instruct and Claude Mythos 5.1 support reasoning and tool use?+

Olmo 3.1 32B Instruct: tool calling. Claude Mythos 5.1: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, Olmo 3.1 32B Instruct or Claude Mythos 5.1?+

Olmo 3.1 32B Instruct has 0 sourced provider routes; Claude Mythos 5.1 has 1, so Claude Mythos 5.1 has broader tracked availability.

Which offers better value, Olmo 3.1 32B Instruct or Claude Mythos 5.1?+

There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.

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